Estimating spatial autoregressions under heteroskedasticity without searching for instruments

Abstract

This paper proposes estimating higher-order spatial autoregressions with spatial autoregressive errors and heteroskedastic error innovations without searching for instruments by explicitly exploiting the endogeneity of spatial lags in the outcome and error equations. The resulting estimator is shown to be consistent and asymptotically normal. Monte Carlo experiments demonstrate that it possesses better finite-sample properties than existing estimators. An empirical study of venture capital funding for biotechnology firms illustrates that spatial correlation stretches as far as 20 miles and that the number of venture capital firms in close proximity has stronger impact on the level of funding than as reported in an existing study.